Datasets
Models
Downloads: 1
1474 lines (1473 with data), 48.4 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.ensemble import ExtraTreesClassifier"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1min 26s, sys: 14.7 s, total: 1min 41s\n",
"Wall time: 2min 45s\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>target</th>\n",
" <th>subject</th>\n",
" <th>chest_ACC_x</th>\n",
" <th>chest_ACC_y</th>\n",
" <th>chest_ACC_z</th>\n",
" <th>chest_ECG</th>\n",
" <th>chest_EMG</th>\n",
" <th>chest_EDA</th>\n",
" <th>chest_Temp</th>\n",
" <th>chest_Resp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.3510</td>\n",
" <td>-0.1906</td>\n",
" <td>2.2842</td>\n",
" <td>0.033371</td>\n",
" <td>0.055344</td>\n",
" <td>9.859848</td>\n",
" <td>33.037903</td>\n",
" <td>3.106689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.4422</td>\n",
" <td>-0.1142</td>\n",
" <td>2.2216</td>\n",
" <td>0.036392</td>\n",
" <td>0.038773</td>\n",
" <td>9.861374</td>\n",
" <td>33.042389</td>\n",
" <td>3.111267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.5322</td>\n",
" <td>-0.0582</td>\n",
" <td>1.9242</td>\n",
" <td>0.040466</td>\n",
" <td>0.016068</td>\n",
" <td>9.861755</td>\n",
" <td>33.051392</td>\n",
" <td>3.126526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.6926</td>\n",
" <td>-0.0010</td>\n",
" <td>1.4022</td>\n",
" <td>0.042526</td>\n",
" <td>0.055206</td>\n",
" <td>9.859848</td>\n",
" <td>32.985321</td>\n",
" <td>3.147888</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.9018</td>\n",
" <td>0.0450</td>\n",
" <td>0.6478</td>\n",
" <td>0.046371</td>\n",
" <td>0.037308</td>\n",
" <td>9.859848</td>\n",
" <td>33.034882</td>\n",
" <td>3.175354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.1470</td>\n",
" <td>0.0654</td>\n",
" <td>-0.2632</td>\n",
" <td>0.047791</td>\n",
" <td>-0.001511</td>\n",
" <td>9.860229</td>\n",
" <td>33.049896</td>\n",
" <td>3.169250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.4090</td>\n",
" <td>0.0568</td>\n",
" <td>-1.2230</td>\n",
" <td>0.046371</td>\n",
" <td>-0.025772</td>\n",
" <td>9.862137</td>\n",
" <td>33.043884</td>\n",
" <td>3.175354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.5896</td>\n",
" <td>0.0172</td>\n",
" <td>-2.1000</td>\n",
" <td>0.044907</td>\n",
" <td>-0.027420</td>\n",
" <td>9.859085</td>\n",
" <td>33.040894</td>\n",
" <td>3.172302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.6616</td>\n",
" <td>-0.0536</td>\n",
" <td>-2.8072</td>\n",
" <td>0.047928</td>\n",
" <td>-0.003983</td>\n",
" <td>9.860611</td>\n",
" <td>33.034882</td>\n",
" <td>3.187561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.6314</td>\n",
" <td>-0.1454</td>\n",
" <td>-3.2726</td>\n",
" <td>0.055344</td>\n",
" <td>-0.008240</td>\n",
" <td>9.859848</td>\n",
" <td>33.049896</td>\n",
" <td>3.208923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.5290</td>\n",
" <td>-0.2498</td>\n",
" <td>-3.4686</td>\n",
" <td>0.062210</td>\n",
" <td>-0.013138</td>\n",
" <td>9.859467</td>\n",
" <td>33.016846</td>\n",
" <td>3.201294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.3880</td>\n",
" <td>-0.3526</td>\n",
" <td>-3.4140</td>\n",
" <td>0.067566</td>\n",
" <td>-0.018494</td>\n",
" <td>9.862900</td>\n",
" <td>33.045380</td>\n",
" <td>3.320312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.2374</td>\n",
" <td>-0.4390</td>\n",
" <td>-3.1510</td>\n",
" <td>0.067200</td>\n",
" <td>-0.028427</td>\n",
" <td>9.860992</td>\n",
" <td>33.040894</td>\n",
" <td>3.218079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1.1054</td>\n",
" <td>-0.5112</td>\n",
" <td>-2.7318</td>\n",
" <td>0.059006</td>\n",
" <td>-0.017670</td>\n",
" <td>9.860229</td>\n",
" <td>33.034882</td>\n",
" <td>3.240967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.9774</td>\n",
" <td>-0.5764</td>\n",
" <td>-2.2456</td>\n",
" <td>0.048386</td>\n",
" <td>-0.015335</td>\n",
" <td>9.860992</td>\n",
" <td>33.031860</td>\n",
" <td>3.009033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.8558</td>\n",
" <td>-0.6306</td>\n",
" <td>-1.7186</td>\n",
" <td>0.046875</td>\n",
" <td>-0.002472</td>\n",
" <td>9.860611</td>\n",
" <td>33.006348</td>\n",
" <td>3.294373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.7516</td>\n",
" <td>-0.6654</td>\n",
" <td>-1.2138</td>\n",
" <td>0.050034</td>\n",
" <td>-0.000549</td>\n",
" <td>9.861374</td>\n",
" <td>33.049896</td>\n",
" <td>3.288269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.6776</td>\n",
" <td>-0.6728</td>\n",
" <td>-0.7410</td>\n",
" <td>0.056534</td>\n",
" <td>0.009567</td>\n",
" <td>9.860611</td>\n",
" <td>33.054413</td>\n",
" <td>3.282166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.6420</td>\n",
" <td>-0.6590</td>\n",
" <td>-0.3346</td>\n",
" <td>0.068436</td>\n",
" <td>0.008011</td>\n",
" <td>9.859848</td>\n",
" <td>33.052917</td>\n",
" <td>3.340149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.6174</td>\n",
" <td>-0.6214</td>\n",
" <td>0.0060</td>\n",
" <td>0.069901</td>\n",
" <td>0.010437</td>\n",
" <td>9.860229</td>\n",
" <td>33.165710</td>\n",
" <td>3.312683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.6028</td>\n",
" <td>-0.5608</td>\n",
" <td>0.2778</td>\n",
" <td>0.055801</td>\n",
" <td>0.002151</td>\n",
" <td>9.862137</td>\n",
" <td>33.040894</td>\n",
" <td>3.314209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.6006</td>\n",
" <td>-0.4818</td>\n",
" <td>0.4694</td>\n",
" <td>0.044037</td>\n",
" <td>-0.005585</td>\n",
" <td>9.859467</td>\n",
" <td>33.028870</td>\n",
" <td>3.320312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.6238</td>\n",
" <td>-0.3798</td>\n",
" <td>0.6010</td>\n",
" <td>0.038132</td>\n",
" <td>0.000320</td>\n",
" <td>9.861374</td>\n",
" <td>33.043884</td>\n",
" <td>3.346252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.6606</td>\n",
" <td>-0.2628</td>\n",
" <td>0.6690</td>\n",
" <td>0.031494</td>\n",
" <td>-0.011215</td>\n",
" <td>9.860611</td>\n",
" <td>33.043884</td>\n",
" <td>3.363037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.7114</td>\n",
" <td>-0.1436</td>\n",
" <td>0.6746</td>\n",
" <td>0.024124</td>\n",
" <td>-0.018082</td>\n",
" <td>9.860229</td>\n",
" <td>33.006348</td>\n",
" <td>3.364563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.7614</td>\n",
" <td>-0.0282</td>\n",
" <td>0.6458</td>\n",
" <td>0.019272</td>\n",
" <td>-0.002701</td>\n",
" <td>9.858704</td>\n",
" <td>33.042389</td>\n",
" <td>3.367615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.7986</td>\n",
" <td>0.0776</td>\n",
" <td>0.5776</td>\n",
" <td>0.027054</td>\n",
" <td>0.000229</td>\n",
" <td>9.859848</td>\n",
" <td>32.995819</td>\n",
" <td>3.395081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.8338</td>\n",
" <td>0.1674</td>\n",
" <td>0.4838</td>\n",
" <td>0.037308</td>\n",
" <td>0.008652</td>\n",
" <td>9.859085</td>\n",
" <td>33.052917</td>\n",
" <td>3.392029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.8654</td>\n",
" <td>0.2430</td>\n",
" <td>0.3646</td>\n",
" <td>0.045639</td>\n",
" <td>-0.000229</td>\n",
" <td>9.856796</td>\n",
" <td>33.042389</td>\n",
" <td>3.422546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0.8906</td>\n",
" <td>0.3022</td>\n",
" <td>0.2294</td>\n",
" <td>0.043076</td>\n",
" <td>0.000961</td>\n",
" <td>9.860229</td>\n",
" <td>33.040894</td>\n",
" <td>3.411865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125470</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9058</td>\n",
" <td>0.0030</td>\n",
" <td>-0.2038</td>\n",
" <td>-0.149597</td>\n",
" <td>-0.021103</td>\n",
" <td>13.101578</td>\n",
" <td>34.917084</td>\n",
" <td>0.666809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125471</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9080</td>\n",
" <td>-0.0026</td>\n",
" <td>-0.2086</td>\n",
" <td>-0.143417</td>\n",
" <td>-0.072601</td>\n",
" <td>13.099670</td>\n",
" <td>34.927887</td>\n",
" <td>0.733948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125472</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9074</td>\n",
" <td>-0.0096</td>\n",
" <td>-0.2084</td>\n",
" <td>-0.124695</td>\n",
" <td>-0.073654</td>\n",
" <td>13.100815</td>\n",
" <td>34.912445</td>\n",
" <td>0.712585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125473</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9030</td>\n",
" <td>-0.0146</td>\n",
" <td>-0.2122</td>\n",
" <td>-0.098282</td>\n",
" <td>-0.034195</td>\n",
" <td>13.099670</td>\n",
" <td>34.921722</td>\n",
" <td>0.715637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125474</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9026</td>\n",
" <td>-0.0238</td>\n",
" <td>-0.2134</td>\n",
" <td>-0.086838</td>\n",
" <td>-0.012955</td>\n",
" <td>13.098526</td>\n",
" <td>34.944824</td>\n",
" <td>0.715637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125475</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9014</td>\n",
" <td>-0.0306</td>\n",
" <td>-0.2184</td>\n",
" <td>-0.079330</td>\n",
" <td>-0.013870</td>\n",
" <td>13.099670</td>\n",
" <td>34.944824</td>\n",
" <td>0.723267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125476</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8982</td>\n",
" <td>-0.0392</td>\n",
" <td>-0.2282</td>\n",
" <td>-0.064499</td>\n",
" <td>-0.027878</td>\n",
" <td>13.097763</td>\n",
" <td>34.930969</td>\n",
" <td>0.723267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125477</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9010</td>\n",
" <td>-0.0472</td>\n",
" <td>-0.2392</td>\n",
" <td>-0.065506</td>\n",
" <td>-0.008652</td>\n",
" <td>13.095856</td>\n",
" <td>34.923248</td>\n",
" <td>0.726318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125478</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9010</td>\n",
" <td>-0.0558</td>\n",
" <td>-0.2504</td>\n",
" <td>-0.082901</td>\n",
" <td>-0.011169</td>\n",
" <td>13.096619</td>\n",
" <td>34.890869</td>\n",
" <td>0.738525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125479</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9034</td>\n",
" <td>-0.0618</td>\n",
" <td>-0.2654</td>\n",
" <td>-0.092606</td>\n",
" <td>0.016708</td>\n",
" <td>13.090515</td>\n",
" <td>34.924805</td>\n",
" <td>0.740051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125480</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9048</td>\n",
" <td>-0.0650</td>\n",
" <td>-0.2760</td>\n",
" <td>-0.093063</td>\n",
" <td>0.031815</td>\n",
" <td>13.100815</td>\n",
" <td>34.943298</td>\n",
" <td>0.744629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125481</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9062</td>\n",
" <td>-0.0658</td>\n",
" <td>-0.2838</td>\n",
" <td>-0.092468</td>\n",
" <td>0.025085</td>\n",
" <td>13.100052</td>\n",
" <td>34.844666</td>\n",
" <td>0.746155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125482</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9074</td>\n",
" <td>-0.0670</td>\n",
" <td>-0.2810</td>\n",
" <td>-0.074936</td>\n",
" <td>-0.010803</td>\n",
" <td>13.098526</td>\n",
" <td>34.914001</td>\n",
" <td>0.746155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125483</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9054</td>\n",
" <td>-0.0626</td>\n",
" <td>-0.2776</td>\n",
" <td>-0.063492</td>\n",
" <td>-0.010117</td>\n",
" <td>13.097763</td>\n",
" <td>34.944824</td>\n",
" <td>0.778198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125484</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9080</td>\n",
" <td>-0.0648</td>\n",
" <td>-0.2614</td>\n",
" <td>-0.044815</td>\n",
" <td>0.032043</td>\n",
" <td>13.091660</td>\n",
" <td>34.920166</td>\n",
" <td>0.755310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125485</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9086</td>\n",
" <td>-0.0630</td>\n",
" <td>-0.2406</td>\n",
" <td>-0.027557</td>\n",
" <td>0.075348</td>\n",
" <td>13.097763</td>\n",
" <td>34.937134</td>\n",
" <td>0.788879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125486</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9058</td>\n",
" <td>-0.0622</td>\n",
" <td>-0.2130</td>\n",
" <td>-0.034241</td>\n",
" <td>0.075256</td>\n",
" <td>13.097763</td>\n",
" <td>34.946381</td>\n",
" <td>0.775146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125487</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9038</td>\n",
" <td>-0.0634</td>\n",
" <td>-0.1842</td>\n",
" <td>-0.044449</td>\n",
" <td>0.010574</td>\n",
" <td>13.098526</td>\n",
" <td>34.904755</td>\n",
" <td>0.764465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125488</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.9018</td>\n",
" <td>-0.0630</td>\n",
" <td>-0.1542</td>\n",
" <td>-0.069534</td>\n",
" <td>-0.091919</td>\n",
" <td>13.088608</td>\n",
" <td>34.906281</td>\n",
" <td>0.773621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125489</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8956</td>\n",
" <td>-0.0654</td>\n",
" <td>-0.1172</td>\n",
" <td>-0.065140</td>\n",
" <td>-0.201828</td>\n",
" <td>13.094711</td>\n",
" <td>34.926331</td>\n",
" <td>0.764465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125490</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8892</td>\n",
" <td>-0.0686</td>\n",
" <td>-0.0914</td>\n",
" <td>-0.033463</td>\n",
" <td>-0.256714</td>\n",
" <td>13.094711</td>\n",
" <td>34.924805</td>\n",
" <td>0.779724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125491</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8858</td>\n",
" <td>-0.0742</td>\n",
" <td>-0.0654</td>\n",
" <td>0.010391</td>\n",
" <td>-0.191208</td>\n",
" <td>13.092804</td>\n",
" <td>34.923248</td>\n",
" <td>0.775146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125492</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8792</td>\n",
" <td>-0.0790</td>\n",
" <td>-0.0486</td>\n",
" <td>0.052826</td>\n",
" <td>-0.093887</td>\n",
" <td>13.090515</td>\n",
" <td>34.929413</td>\n",
" <td>0.621033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125493</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8714</td>\n",
" <td>-0.0824</td>\n",
" <td>-0.0390</td>\n",
" <td>0.099106</td>\n",
" <td>-0.048386</td>\n",
" <td>13.088608</td>\n",
" <td>34.921722</td>\n",
" <td>0.785828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125494</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8650</td>\n",
" <td>-0.0878</td>\n",
" <td>-0.0344</td>\n",
" <td>0.143417</td>\n",
" <td>-0.064957</td>\n",
" <td>13.086700</td>\n",
" <td>34.915527</td>\n",
" <td>0.730896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125495</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8586</td>\n",
" <td>-0.0958</td>\n",
" <td>-0.0390</td>\n",
" <td>0.156418</td>\n",
" <td>-0.036942</td>\n",
" <td>13.085556</td>\n",
" <td>34.917084</td>\n",
" <td>0.813293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125496</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8526</td>\n",
" <td>-0.1026</td>\n",
" <td>-0.0430</td>\n",
" <td>0.115723</td>\n",
" <td>0.098831</td>\n",
" <td>13.089371</td>\n",
" <td>34.935608</td>\n",
" <td>0.816345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125497</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8466</td>\n",
" <td>-0.1082</td>\n",
" <td>-0.0574</td>\n",
" <td>0.086380</td>\n",
" <td>0.199768</td>\n",
" <td>13.090134</td>\n",
" <td>34.943298</td>\n",
" <td>0.804138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125498</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8422</td>\n",
" <td>-0.1138</td>\n",
" <td>-0.0814</td>\n",
" <td>0.139938</td>\n",
" <td>0.227646</td>\n",
" <td>13.081360</td>\n",
" <td>34.940247</td>\n",
" <td>0.834656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59125499</th>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0.8334</td>\n",
" <td>-0.1152</td>\n",
" <td>-0.1026</td>\n",
" <td>0.238998</td>\n",
" <td>0.218079</td>\n",
" <td>13.080215</td>\n",
" <td>34.947906</td>\n",
" <td>0.837708</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>59125500 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" target subject chest_ACC_x chest_ACC_y chest_ACC_z chest_ECG \\\n",
"0 0 6 0.3510 -0.1906 2.2842 0.033371 \n",
"1 0 6 0.4422 -0.1142 2.2216 0.036392 \n",
"2 0 6 0.5322 -0.0582 1.9242 0.040466 \n",
"3 0 6 0.6926 -0.0010 1.4022 0.042526 \n",
"4 0 6 0.9018 0.0450 0.6478 0.046371 \n",
"... ... ... ... ... ... ... \n",
"59125495 0 7 0.8586 -0.0958 -0.0390 0.156418 \n",
"59125496 0 7 0.8526 -0.1026 -0.0430 0.115723 \n",
"59125497 0 7 0.8466 -0.1082 -0.0574 0.086380 \n",
"59125498 0 7 0.8422 -0.1138 -0.0814 0.139938 \n",
"59125499 0 7 0.8334 -0.1152 -0.1026 0.238998 \n",
"\n",
" chest_EMG chest_EDA chest_Temp chest_Resp \n",
"0 0.055344 9.859848 33.037903 3.106689 \n",
"1 0.038773 9.861374 33.042389 3.111267 \n",
"2 0.016068 9.861755 33.051392 3.126526 \n",
"3 0.055206 9.859848 32.985321 3.147888 \n",
"4 0.037308 9.859848 33.034882 3.175354 \n",
"... ... ... ... ... \n",
"59125495 -0.036942 13.085556 34.917084 0.813293 \n",
"59125496 0.098831 13.089371 34.935608 0.816345 \n",
"59125497 0.199768 13.090134 34.943298 0.804138 \n",
"59125498 0.227646 13.081360 34.940247 0.834656 \n",
"59125499 0.218079 13.080215 34.947906 0.837708 \n",
"\n",
"[59125500 rows x 10 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"df = pd.read_csv(\"master_data.csv\")\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['chest_ACC_x',\n",
" 'chest_ACC_y',\n",
" 'chest_ACC_z',\n",
" 'chest_ECG',\n",
" 'chest_EMG',\n",
" 'chest_EDA',\n",
" 'chest_Temp',\n",
" 'chest_Resp']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features=df.columns.tolist()\n",
"to_remove = [fea for fea in features if \"target\" in fea or \"subject\" in fea]\n",
"feature = [x for x in features if x not in to_remove]\n",
"feature"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"feature=['chest_ACC_x',\n",
" 'chest_ACC_y',\n",
" 'chest_ACC_z',\n",
" 'chest_EDA',\n",
" 'chest_Temp']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"subjects=[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17]\n",
"cls=[0,1,2,3,4]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"for i in subjects:\n",
" globals()['subject_%s' % i]=df[df['subject'] == i]\n",
" for j in cls:\n",
" globals()['subject_{}_{}'.format(i,j)]=globals()['subject_%s' % i][globals()['subject_%s' % i]['target'] == j]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ExtraTreesClassifier\t RandomForestClassifier\t classification_report\t cls\t df\t feature\t features\t i\t j\t \n",
"no_of_rows\t pd\t subject_10\t subject_10_0\t subject_10_0_test\t subject_10_0_train\t subject_10_1\t subject_10_1_test\t subject_10_1_train\t \n",
"subject_10_2\t subject_10_2_test\t subject_10_2_train\t subject_10_3\t subject_10_3_test\t subject_10_3_train\t subject_10_4\t subject_10_4_test\t subject_10_4_train\t \n",
"subject_11\t subject_11_0\t subject_11_0_test\t subject_11_0_train\t subject_11_1\t subject_11_1_test\t subject_11_1_train\t subject_11_2\t subject_11_2_test\t \n",
"subject_11_2_train\t subject_11_3\t subject_11_3_test\t subject_11_3_train\t subject_11_4\t subject_11_4_test\t subject_11_4_train\t subject_13\t subject_13_0\t \n",
"subject_13_0_test\t subject_13_0_train\t subject_13_1\t subject_13_1_test\t subject_13_1_train\t subject_13_2\t subject_13_2_test\t subject_13_2_train\t subject_13_3\t \n",
"subject_13_3_test\t subject_13_3_train\t subject_13_4\t subject_13_4_test\t subject_13_4_train\t subject_14\t subject_14_0\t subject_14_0_test\t subject_14_0_train\t \n",
"subject_14_1\t subject_14_1_test\t subject_14_1_train\t subject_14_2\t subject_14_2_test\t subject_14_2_train\t subject_14_3\t subject_14_3_test\t subject_14_3_train\t \n",
"subject_14_4\t subject_14_4_test\t subject_14_4_train\t subject_15\t subject_15_0\t subject_15_0_test\t subject_15_0_train\t subject_15_1\t subject_15_1_test\t \n",
"subject_15_1_train\t subject_15_2\t subject_15_2_test\t subject_15_2_train\t subject_15_3\t subject_15_3_test\t subject_15_3_train\t subject_15_4\t subject_15_4_test\t \n",
"subject_15_4_train\t subject_16\t subject_16_0\t subject_16_0_test\t subject_16_0_train\t subject_16_1\t subject_16_1_test\t subject_16_1_train\t subject_16_2\t \n",
"subject_16_2_test\t subject_16_2_train\t subject_16_3\t subject_16_3_test\t subject_16_3_train\t subject_16_4\t subject_16_4_test\t subject_16_4_train\t subject_17\t \n",
"subject_17_0\t subject_17_0_test\t subject_17_0_train\t subject_17_1\t subject_17_1_test\t subject_17_1_train\t subject_17_2\t subject_17_2_test\t subject_17_2_train\t \n",
"subject_17_3\t subject_17_3_test\t subject_17_3_train\t subject_17_4\t subject_17_4_test\t subject_17_4_train\t subject_2\t subject_2_0\t subject_2_0_test\t \n",
"subject_2_0_train\t subject_2_1\t subject_2_1_test\t subject_2_1_train\t subject_2_2\t subject_2_2_test\t subject_2_2_train\t subject_2_3\t subject_2_3_test\t \n",
"subject_2_3_train\t subject_2_4\t subject_2_4_test\t subject_2_4_train\t subject_2_test\t subject_3\t subject_3_0\t subject_3_0_test\t subject_3_0_train\t \n",
"subject_3_1\t subject_3_1_test\t subject_3_1_train\t subject_3_2\t subject_3_2_test\t subject_3_2_train\t subject_3_3\t subject_3_3_test\t subject_3_3_train\t \n",
"subject_3_4\t subject_3_4_test\t subject_3_4_train\t subject_3_test\t subject_4\t subject_4_0\t subject_4_0_test\t subject_4_0_train\t subject_4_1\t \n",
"subject_4_1_test\t subject_4_1_train\t subject_4_2\t subject_4_2_test\t subject_4_2_train\t subject_4_3\t subject_4_3_test\t subject_4_3_train\t subject_4_4\t \n",
"subject_4_4_test\t subject_4_4_train\t subject_4_test\t subject_5\t subject_5_0\t subject_5_0_test\t subject_5_0_train\t subject_5_1\t subject_5_1_test\t \n",
"subject_5_1_train\t subject_5_2\t subject_5_2_test\t subject_5_2_train\t subject_5_3\t subject_5_3_test\t subject_5_3_train\t subject_5_4\t subject_5_4_test\t \n",
"subject_5_4_train\t subject_5_test\t subject_6\t subject_6_0\t subject_6_0_test\t subject_6_0_train\t subject_6_1\t subject_6_1_test\t subject_6_1_train\t \n",
"subject_6_2\t subject_6_2_test\t subject_6_2_train\t subject_6_3\t subject_6_3_test\t subject_6_3_train\t subject_6_4\t subject_6_4_test\t subject_6_4_train\t \n",
"subject_6_test\t subject_7\t subject_7_0\t subject_7_0_test\t subject_7_0_train\t subject_7_1\t subject_7_1_test\t subject_7_1_train\t subject_7_2\t \n",
"subject_7_2_test\t subject_7_2_train\t subject_7_3\t subject_7_3_test\t subject_7_3_train\t subject_7_4\t subject_7_4_test\t subject_7_4_train\t subject_7_test\t \n",
"subject_8\t subject_8_0\t subject_8_0_test\t subject_8_0_train\t subject_8_1\t subject_8_1_test\t subject_8_1_train\t subject_8_2\t subject_8_2_test\t \n",
"subject_8_2_train\t subject_8_3\t subject_8_3_test\t subject_8_3_train\t subject_8_4\t subject_8_4_test\t subject_8_4_train\t subject_8_test\t subject_9\t \n",
"subject_9_0\t subject_9_0_test\t subject_9_0_train\t subject_9_1\t subject_9_1_test\t subject_9_1_train\t subject_9_2\t subject_9_2_test\t subject_9_2_train\t \n",
"subject_9_3\t subject_9_3_test\t subject_9_3_train\t subject_9_4\t subject_9_4_test\t subject_9_4_train\t subjects\t test\t test_2_list\t \n",
"test_list\t to_remove\t train\t train_list\t train_test_split\t \n"
]
}
],
"source": [
"who\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"no_of_rows=700\n",
"for i in subjects:\n",
" for j in cls:\n",
" globals()['subject_{}_{}_train'.format(i,j)]=globals()['subject_{}_{}'.format(i,j)].iloc[:no_of_rows]\n",
" globals()['subject_{}_{}_test'.format(i,j)]=globals()['subject_{}_{}'.format(i,j)].iloc[no_of_rows:]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"\n",
"train_list=[]\n",
"test_list=[]\n",
"for i in subjects:\n",
" for j in cls:\n",
" train_list.append(globals()['subject_{}_{}_train'.format(i,j)])\n",
" test_list.append(globals()['subject_{}_{}_test'.format(i,j)])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"52500\n"
]
}
],
"source": [
"train=pd.concat(train_list,ignore_index=True)\n",
"test=pd.concat(test_list,ignore_index=True)\n",
"print(len(list(train['target'])))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"52500"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"700*5*15"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 3, 4]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"subjects[:3]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"\n",
"test_2_list=[]\n",
"for i in subjects[:3]:\n",
" test_2_list=[]\n",
" for j in cls:\n",
" test_2_list.append(globals()['subject_{}_{}_test'.format(i,j)])\n",
" globals()['subject_{}_test'.format(i)]=pd.concat(test_2_list,ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"del df\n",
"for i in subjects[2:]:\n",
" del(globals()['subject_%s' % i])\n",
" for j in cls:\n",
" del(globals()['subject_{}_{}'.format(i,j)])\n",
" del(globals()['subject_{}_{}_train'.format(i,j)])\n",
" del(globals()['subject_{}_{}_test'.format(i,j)])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 65 µs, sys: 20 µs, total: 85 µs\n",
"Wall time: 88.5 µs\n"
]
}
],
"source": [
"%%time\n",
"et = ExtraTreesClassifier(n_estimators=50, n_jobs=10, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
"[Parallel(n_jobs=10)]: Done 21 tasks | elapsed: 0.2s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"building tree 1 of 50building tree 2 of 50\n",
"building tree 3 of 50\n",
"building tree 4 of 50\n",
"building tree 5 of 50\n",
"building tree 6 of 50\n",
"\n",
"building tree 7 of 50\n",
"building tree 8 of 50\n",
"building tree 9 of 50\n",
"building tree 10 of 50\n",
"building tree 11 of 50building tree 12 of 50\n",
"\n",
"building tree 13 of 50\n",
"building tree 14 of 50\n",
"building tree 15 of 50\n",
"building tree 16 of 50\n",
"building tree 17 of 50\n",
"building tree 18 of 50\n",
"building tree 19 of 50\n",
"building tree 20 of 50\n",
"building tree 21 of 50\n",
"building tree 22 of 50\n",
"building tree 23 of 50\n",
"building tree 24 of 50building tree 25 of 50\n",
"\n",
"building tree 26 of 50building tree 27 of 50\n",
"\n",
"building tree 28 of 50\n",
"building tree 29 of 50\n",
"building tree 30 of 50building tree 31 of 50\n",
"\n",
"building tree 32 of 50\n",
"building tree 33 of 50building tree 34 of 50\n",
"building tree 35 of 50\n",
"\n",
"building tree 36 of 50\n",
"building tree 37 of 50\n",
"building tree 38 of 50\n",
"building tree 39 of 50\n",
"building tree 40 of 50\n",
"building tree 41 of 50\n",
"building tree 42 of 50\n",
"building tree 43 of 50\n",
"building tree 44 of 50\n",
"building tree 45 of 50building tree 46 of 50\n",
"building tree 47 of 50\n",
"\n",
"building tree 48 of 50building tree 49 of 50\n",
"\n",
"building tree 50 of 50\n",
"CPU times: user 1.08 s, sys: 156 ms, total: 1.24 s\n",
"Wall time: 337 ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=10)]: Done 50 out of 50 | elapsed: 0.3s finished\n"
]
},
{
"data": {
"text/plain": [
"ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=10,\n",
" oob_score=False, random_state=None, verbose=2,\n",
" warm_start=False)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"et.fit(train[feature],train['target'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
"[Parallel(n_jobs=10)]: Done 21 tasks | elapsed: 2.5s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 21.9 s, sys: 7.22 s, total: 29.1 s\n",
"Wall time: 5.25 s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=10)]: Done 50 out of 50 | elapsed: 4.8s finished\n"
]
}
],
"source": [
"%%time \n",
"y_pred=et.predict(subject_2_test[feature])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.49 0.19 0.27 2142001\n",
" 1 0.77 0.76 0.76 800100\n",
" 2 0.31 0.99 0.47 429800\n",
" 3 0.27 0.60 0.38 252700\n",
" 4 0.42 0.51 0.46 536899\n",
"\n",
" accuracy 0.44 4161500\n",
" macro avg 0.45 0.61 0.47 4161500\n",
"weighted avg 0.50 0.44 0.42 4161500\n",
"\n",
"CPU times: user 5.31 s, sys: 0 ns, total: 5.31 s\n",
"Wall time: 5.31 s\n"
]
}
],
"source": [
"%%time\n",
"print(classification_report(subject_2_test['target'], y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
"[Parallel(n_jobs=10)]: Done 21 tasks | elapsed: 2.9s\n",
"[Parallel(n_jobs=10)]: Done 50 out of 50 | elapsed: 5.6s finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 26.7 s, sys: 8.06 s, total: 34.8 s\n",
"Wall time: 6.16 s\n"
]
}
],
"source": [
"%%time \n",
"y_pred=et.predict(subject_3_test[feature])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.38 0.30 0.34 2344999\n",
" 1 0.00 0.00 0.00 797300\n",
" 2 0.20 0.27 0.23 447300\n",
" 3 0.34 0.98 0.51 261800\n",
" 4 0.66 0.52 0.58 545301\n",
"\n",
" accuracy 0.31 4396700\n",
" macro avg 0.32 0.42 0.33 4396700\n",
"weighted avg 0.32 0.31 0.31 4396700\n",
"\n",
"CPU times: user 5.62 s, sys: 1.64 s, total: 7.26 s\n",
"Wall time: 7.26 s\n"
]
}
],
"source": [
"%%time\n",
"print(classification_report(subject_3_test['target'], y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\n",
"[Parallel(n_jobs=10)]: Done 21 tasks | elapsed: 2.8s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 27 s, sys: 7.7 s, total: 34.7 s\n",
"Wall time: 6.15 s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=10)]: Done 50 out of 50 | elapsed: 5.6s finished\n"
]
}
],
"source": [
"%%time \n",
"y_pred=et.predict(subject_4_test[feature])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.70 0.81 0.75 2313499\n",
" 1 0.71 0.79 0.75 809901\n",
" 2 0.20 0.17 0.18 443800\n",
" 3 0.32 0.21 0.25 259700\n",
" 4 0.96 0.49 0.65 562800\n",
"\n",
" accuracy 0.66 4389700\n",
" macro avg 0.58 0.49 0.52 4389700\n",
"weighted avg 0.66 0.66 0.65 4389700\n",
"\n",
"CPU times: user 5.6 s, sys: 1.66 s, total: 7.25 s\n",
"Wall time: 7.25 s\n"
]
}
],
"source": [
"%%time\n",
"print(classification_report(subject_4_test['target'], y_pred))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}